BACKGROUNDS To find early liver cancer, the ministry of health and welfare has conducted surveillance targeting high-risk patients. In 2017, the incidence rate of liver cancer in surveillance was 0.9%, suggesting that a broad patient group was included in surveillance. In this study, to reduce surveillance patients, a prediction model with zero-falsenegative was developed using a machine learning. METHODS To develop the model, we used 2016 Health Insurance Review & Assessment Service-National Patients Sample utilized to the Common Data Model (CDM). This study targeted patients who did not have a severe condition of liver cancer in surveillance. The number of the target was 13,703 cases. The covariates for the model were identified by a chi-square test conducted on gender, age group, condition between a case and control group. LASSO was performed to develop the model. RESULTS Gender, age group, forty diseases were selected as a covariate. The model has an AUC of 0.745, a negative rate of 4.0%, a specificity of 4.5%, and a PPV of 11.8% with zerofalse- negative. CONCLUSION It might be possible to refine surveillance and save the budget of the National Health Insurance Service, and governments.
목차
Abstract 서론 연구 방법 1. 자료원 2. 연구대상 3. 예측 모델 개발 결과 1. 연구대상자의 특징 2. 예측 모델 고찰 결론 감사의 말씀 References
키워드
Common data modelMachine learningHepatocellular carcinomaPrediction modelSurveillance
저자
이명철 [ Myeongcheol Lee | 경희대학교 약학대학 ]
최경선 [ Kyungseon Choi | 경희대학교 규제과학과 ]
서혜선 [ Hae Sun Suh | 경희대학교 약학대학, 경희대학교 규제과학과 ]
Corresponding Author
한국보건사회약료경영학회 [Korean Academy of Social & Managed Care Phemacy]
설립연도
2009
분야
의약학>약학
소개
본 학회는 비영리 학술단체로서 환자의 삶의 질 향상을 위한 약료(pharmaceutical care)가 보장될 수 있도록 약료경영약학(managed care pharmacy)을 발전시키는 데 궁극 목적을 두고 다음과 같은 활동을 전개하여 사회에 기여하며 회원 상호간의 친목을 도모한다.
간행물
간행물명
한국보건사회약료경영학회지 [Journal of Korean Academy of Managed Care Pharmacy]